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The accuracy of climate projections is improved by increasing the number of samples from ensemble experiments, leading to a decrease in the confidence interval of a target climatological variable. The improvement in the accuracy depends on the degree of independence of each ensemble member in the experiments. When the members of ensemble experiments are dependent on each other, the introduction of an effective sample size (ESS) is necessary to correctly estimate the confidence interval. This study is the first attempt to estimate the ESS for precipitation as a function of the number of ensemble members, although some previous studies have investigated another type of ESS in terms of the length of simulation period. The ESS in the present study is intrinsic to the atmospheric general circulation models (AGCM) forced by the ocean boundary condition because the outputs of AGCM ensemble members are similar or dependent on each other due to the commonly used boundary condition, i.e., the distribution of sea surface temperature, sea ice concentration, and sea ice thickness. Looking at the values of ESS as a function of geographical location, those in the tropics and over the ocean are smaller than those at higher latitudes and over continents; precipitation events in areas with smaller (larger) ESS are strongly (weakly) constrained by the ocean boundary condition. The increase in temporal and spatial averaging scales for precipitation estimation leads to the decrease in the ESS, of whose trend is attributed to the spatio-temporal characteristics of the precipitation events as represented by the power spectrum and co-spectrum.

Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.

Future climate analogues were identified for six capital cities in western South America using a novel nonparametric technique and ensemble experiments. We applied the MRI-AGCM3.2H model with a horizontal resolution of approximately 60 km, three convection schemes, four sea surface temperature distributions, and two initial conditions. All ensemble experiments were conducted under scenario A1B of the Special Report on Emissions Scenarios, in which cumulative emissions are similar to those of the RCP 6.0 scenario. The majority of future analogue cities were at lower latitudes than their respective target cities. In general, all analogues of target cities had similarity scores of 0.1-0.3. Of the six analogues, four were located in central and southern Africa, whereas the remaining two were located in western South America. Projected seasonal variations in surface air temperature and precipitation in Santiago, Chile are similar to the current climate in Cape Town, South Africa, and the climate analogue for La Paz, Bolivia, is found in Oruro, Bolivia. The non-parametric method used in this study can be applied to a variety of impact assessments under a changing global climate.

An extremely heavy rainfall occurred over wide areas of Japan from 28 June to 8 July 2018. Heavily damaged areas were distributed from western Japan to Tokai region. This event was officially named the “Heavy Rain Event of July 2018” by the Japan Meteorological Agency. This paper provides a meteorological overview of the event. A comparison with other heavy rainfall events that have occurred since 1982 showed that the heavy rainfall event of 2018 was characterized by rainfall that was unusually widespread spatially and persistent temporally. Factors primarily responsible for this event included the prolonged concentration of two very moist airstreams over western Japan and persistent upward flow associated with activation of the stationary Baiu front. In some areas, line-shaped precipitation systems led to locally anomalous precipitation totals.

Targeting to East Asian summer monsoon for the first time, this study presents an assessment of projection uncertainty in ensemble dynamical downscaling (DDS) simulations. Based on 12-member DDS simulations comprised of three global climate models (GCMs) and four regional climate models (RCMs), we evaluate contributions of GCM and RCM uncertainty to the total uncertainty of summer-time precipitation projections around Japan. Our results show that contribution of RCM uncertainty can be comparable to that of GCM uncertainty in magnitudes. This finding draws a distinction from the past studies showing the dominance of GCM uncertainty. Most notably, our results show that RCM uncertainty for number of precipitating days appears around and over the land. RCM uncertainty for precipitation amounts also shows a dependence on topography but to a lessor degree. These RCM uncertainty characteristics are potentially linked to the difference in various RCM configurations such as physics schemes and model topography. In contrast, GCM uncertainty mostly appears over the ocean, which can be attributed to the difference in the GCM's future projections of East Asian summer monsoon. Our finding may be of an importance for water disaster and water resource management with DDS.

Recent studies have revealed that an increase in surface air temperature elevates the intensity of extreme precipitation associated with the increase of water vapor in the atmosphere, according to the principle of the Clausius–Clapeyron (CC) relationship. In this study, (1) we have verified the dependence of extreme precipitation intensity on temperature (CC relationship) under current climate and (2) investigated the projected changes of the CC relationship over Japan by using multi-model ensemble downscaling experiments of three Regional Climate Models (RCMs) (NHRCM, NRAMS, WRF) forced by JRA25, as well as three General Circulation Models (GCMs) (CCSM4, MIROC5, MRI-CGCM3). Simulated extreme precipitation linked to temperatures from ensemble experiments coincides with observations that place peak temperatures around 19–22 °C. Climate scenarios (RCP4.5) of the late twenty-first century suggest a 2 °C increase of 2 m air temperature, an increase in precipitation intensities above 15 mm/day, and a decrease in weaker precipitation intensities of 10–15 mm/day. The projected change rate of the mean precipitation intensities per mean change in air temperature over Japan is found to be 2.4%/°C. Extreme precipitation intensity increases with temperatures up to 22 °C in future climate scenarios, while the peak is 20 °C for the current climate. Extreme precipitation intensities at higher percentiles are projected to have larger rates of increase (3–5%/°C in the current climate and 4–6%/°C in the future climate scenarios). A decrease of precipitation intensity at higher temperatures relates to water vapor availability. An insufficient water vapor supply for saturation at higher temperatures can lead to a decrease in cloud formation and extreme precipitation.

Rapid expansion of exotic bamboos has lowered species diversity in Japan's ecosystems by hampering native plant growth. The invasive potential of bamboo, facilitated by global warming, may also affect other countries with developing bamboo industries. We examined past (1975–1980) and recent (2012) distributions of major exotic bamboos (Phyllostachys edulis and P. bambusoides) in areas adjacent to 145 weather stations in central and northern Japan. Bamboo stands have been established at 17 sites along the latitudinal and altitudinal distributional limit during the last three decades. Ecological niche modeling indicated that temperature had a strong influence on bamboo distribution. Using mean annual temperature and sun radiation data, we reproduced bamboo distribution (accuracy = 0.93 and AUC (area under the receiver operating characteristic curve) = 0.92). These results infer that exotic bamboo distribution has shifted northward and upslope, in association with recent climate warming. Then, we simulated future climate data and projected the climate change impact on the potential habitat distribution of invasive bamboos under different temperature increases (i.e., 1.5°C, 2.0°C, 3.0°C, and 4.0°C) relative to the preindustrial period. Potential habitats in central and northern Japan were estimated to increase from 35% under the current climate (1980–2000) to 46%–48%, 51%–54%, 61%–67%, and 77%–83% under 1.5°C, 2.0°C, 3.0°C, and 4.0°C warming levels, respectively. These infer that the risk areas can increase by 1.3 times even under a 1.5°C scenario and expand by 2.3 times under a 4.0°C scenario. For sustainable ecosystem management, both mitigation and adaptation are necessary: bamboo planting must be carefully monitored in predicted potential habitats, which covers most of Japan.

Intense tropical cyclones (TCs) sometimes cause huge disasters, so it is imperative to explore the impacts of climate change on such TCs. Therefore, the authors conducted numerical simulations of the most destructive historical TC in Japanese history, Typhoon Vera (1959), in the current climate and a global warming climate. The authors used four nonhydrostatic models with a horizontal resolution of 5 km: the cloud-resolving storm simulator, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, the Japan Meteorological Agency (JMA) operational nonhydrostatic mesoscale model, and the Weather Research and Forecasting Model. Initial and boundary conditions for the control simulation were provided by the Japanese 55-year Reanalysis dataset. Changes between the periods of 1979–2003 and 2075–99 were estimated from climate runs of a 20-km-mesh atmospheric general circulation model, and these changes were added to the initial and boundary conditions of the control simulation to produce the future climate conditions. Although the representation of inner-core structures varies largely between the models, all models project an increase in the maximum intensity of future typhoons. It is found that structural changes only appeared around the storm center with sudden changes in precipitation and near-surface wind speeds as the radius of maximum wind speed (RMW) contracted. In the future climate, the water vapor mixing ratio in the lower troposphere increased by 3–4 g kg−1. The increased water vapor allowed the eyewall updrafts to form continuously inside the RMW and contributed to rapid condensation in the taller and more intense updrafts.

An unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.

This study aims to provide a basis for understanding the mechanisms of diurnal rainfall variability over Peninsular Malaysia by utilising the Non-Hydrostatic Regional Climate Model (NHRCM). The present day climate simulations at 5 km resolution over a period of 20 years, from 1st December 1989 to 31st January 2010 were conducted using the six-hourly Japanese re-analysis 55 years (JRA-55) data and monthly Centennial in situ Observation Based Estimates (COBE) of sea surface temperature as lateral and lower boundary conditions. Despite some biases, the NHRCM performed reasonably well in simulating diurnal rainfall cycles over Peninsular Malaysia. During inter-monsoon periods, the availability of atmospheric moisture played a major role in modulating afternoon rainfall maxima over the foothills of the Titiwangsa mountain range (FT sub-region). During the southwest monsoon, a lack of atmospheric moisture inhibits the occurrence of convective rainfall over the FT sub-region. The NHRCM was also able to simulate the suppression of the diurnal rainfall cycle over the east coast of Peninsular Malaysia (EC sub-region) and afternoon rainfall maximum over the Peninsular Malaysia inland-valley (IN sub-region) area during the northeast monsoon. Over the EC sub-region, daytime radiational warming of the top of clouds enhanced atmospheric stability, thus reducing afternoon rainfall. On the other hand, night-time radiational cooling from cloud tops decreases atmospheric stability and increases nocturnal rainfall. In the early morning, the rainfall maximum was confined to the EC sub-region due to the retardation of the north-easterly monsoonal wind by the land breeze and orographic blocking. However, in the afternoon, superimposition of the sea breeze on the north-easterly monsoonal wind strengthened the north-easterly wind, thus causing the zone of convection to expand further inland.

Additional file 1: Table S1. CMIP5 models used in this study. (Retrieved from https://cmip.llnl.gov/cmip5/availability.html on May 23, 2019). Figure S1. As in Fig. 4 but for (a) JJA mean precipitation and (b) DJF mean precipitation. Figure S2. As in Fig. 5 but for (a) JJA mean precipitation and (b) DJF mean precipitation (%). Figure S3. As in Fig. 6 but for the AMIP-type simulations instead of the CMIP5 simulations. Black number corresponds to the number in the list of models at the bottom. Gray number denotes the rank in Fig. 6 only for the model of which the name coincides with that in Table S1. Figure S4. As in Fig. 7 but for JJA mean precipitation (% K–1). White in (a–d) and hatching in (e–j) indicate the grid cells where the models simulate no precipitation in the present-day climate. Figure S5. As in Fig. 7 but for DJF mean precipitation (% K–1). White in (a–d) and hatching in (e–j) indicate the grid cells where the models simulate no precipitation in the present-day climate. Figure S6. As in Fig. 8 but for (a) JJA mean precipitation and (b) DJF mean precipitation. Hatching indicates the grid cells where the models simulate no precipitation in the present-day climate. Figure S7. As in Fig. 9 but for (a) JJA mean precipitation and (b) DJF mean precipitation (% K–1). Figure S8. Future change anomaly for each of 12 HF ensemble members from their mean. (a) Future change of geopotential height at 500 hPa and (b) that of sea level pressure. Members on each row are the member with the same SST-change pattern for the future climate simulation. Members on each column are the member with the same cumulus convection scheme. The anomaly is normalized by the annual variation of individual variables in the present-day climate. Crosshatching (Hatching) indicates the 99% (90%) confidence level. Figure S9. Standard deviation in future changes of sea level pressure using three clusters of the CMIP5 AOGCMs and all of them, which were created to obtain future change patterns of sea surface temperature and sea ice for the MRI-AGCM3.2 simulations by Mizuta et al. (2014). The deviation is normalized by the annual variation in the present-day climate averaged over the CMIP5 models in each cluster. (see text for details).

This study investigates future changes in the accumulated and daily heavy winter snowfall in central Japan and the surrounding regions. We analyze outputs of the 48-member ensemble regional climate simulations in the historical and future climates. In the historical climate simulations, each ensemble member has a 61-year simulation from September 1950 to August 2011. For the future climate simulations, we also conduct 61-year simulations assuming the climate at the end of the twenty-first century (2080–2099) when the global mean surface air temperature is about 4 °C warmer than the pre-industrial climate (1861–1880) as projected under the Representative Concentration Pathway (RCP) 8.5 scenario. Our simulations show that the heavy snowfall occurring at a frequency of every 10 years is enhanced in the inland areas of the central part of the Japanese archipelago (central Japan) where the total winter snowfall amount decreases significantly. Heavy snowfall is also intensified in the northern part of the Asian continent where the surface air temperature is much colder than over central Japan. A composite analysis of heavy snowfall events in central Japan indicates that such events occur when the Japan Sea polar air mass convergence zone (JPCZ) appears during the East Asian winter monsoon season. In the future climate projections, the JPCZ is intensified since the warm ocean supplies more moisture due to warming. An upward wind anomaly is also found over the windward side of mountains where the upward flow is prevalent climatologically. The intensification of both the JPCZ and the upward wind over the mountain ranges result in the enhancement of heavy snowfall in inland areas where the surface air temperature is still below 0 °C.

As surface warming threatens rice production in temperate climates, the importance of cool regions is increasing. Cultivar choice is an important adaptation option for coping with cli- mate change but is generally evaluated with a single metric for a few hypothetical cultivars. Here, we evaluate adaptation to climate change based on multiple metrics and cultivars in presently cool climates in Japan. We applied the outputs of a global climate model (MIROC5) with a Repre- sentative Concentration Pathways 4.5 scenario, dynamically downscaled to a 10 km mesh for the present (1981�2000) and future (2081�2099) climate conditions. The data were input into a rice- growth model, and the performances of 10 major cultivars were compared in each mesh. With the present-day leading cultivars, the model predicted reduced low-temperature stress, a regional average yield increase of 17%, and several occurrences of high-temperature stress. The most suit- able cultivars in each grid cell changed dramatically because of climate change when a single metric was used as a criterion, and the yield advantage increased to 26%. When yield, cold, and heat stress were taken into account, however, the currently leading cultivars maintained superi- ority in 64% of the grid cells, with an average regional yield gain of 22%, suggesting a require- ment for developing new cultivars by pyramiding useful traits. A trait such as low sensitivity to temperature for phenology helps in ensuring stable growth under variable temperatures. Increas- ing photoperiod sensitivity can be an option under future climates in relatively warmer regions.

A physical vegetation model [the Two-Layer Model (2LM)] was applied to estimate the climate change impacts on rice leaf wetness (LW) as a potential indicator of rice blast occurrence. Japan was used as an example. Dynamically downscaled data at 20-km-mesh resolution from three global climate models (CCSM4, MIROC5, and MRI-CGCM3) were utilized for present (1981–2000) and future (2081–2100) climates under the representative concentration pathway 4.5 scenario. To evaluate the performance of the 2LM, the LW and other meteorological variables were observed for 108 days during the summer of 2013 at three sites on the Pacific Ocean side of Japan. The derived correct estimation rate was 77.4%, which is similar to that observed in previous studies. Using the downscaled dataset, the changes in several precipitation indices were calculated. The regionally averaged ensemble mean precipitation increased by 6%, although large intermodel differences were found. By defining a wet day as any day in which the daily precipitation was ≥ 1 mm day−1, it was found that the precipitation frequency decreased by 6% and the precipitation intensity increased by 11% for the entire area. The leaf surface environment was estimated to be dry; leaf wetness, wet frequency, and wet times all decreased. It was found that a decrease in water trap opportunities due to reduced precipitation frequency was the primary contributor to the LW decrease. For blast fungus, an increased precipitation intensity was expected to enhance the washout effect on the leaf surface. In the present case, the infection risk was estimated to decrease for Japan.

Climate analogues (CAs), regions whose present climates are similar to the future climate of a target place, are identified to assess the effects of climate change towards the end of the 21st century. The location of CAs and their present climates yield information that may be used to mitigate the harmful impacts of climate change. Present (1979–2003) and future (2075–2099) climates are projected in an ensemble experiment using the atmospheric general circulation model of the Meteorological Research Institute in Japan. The ensemble members consist of combinations of four distributions of sea surface temperature, three convection schemes, and two initial conditions. The emission scenario for greenhouse gases is A1B of the Special Report on Emissions Scenarios. A new method to identify the location of CAs is introduced, in which the uncertainty in the climate projection is taken into account. CAs for all land regions of the world are presented and those of four capital cities are analyzed in detail. The CAs are generally distributed equator-ward from the target places, consistent with the global warming. It is also found that ensemble experiments that encompass the uncertainty of climate projections can yield robust results for the CA and lead to reliable assessments of climate change.

Recently, local governments have an increasing need to take extensive and effective local measures to adapt to regional climate change, but have difficulty knowing how and when to adapt to such change. This study aims: 1) to characterize an efficient and cost-effective database management tool (DMT) for developing a Geographic Information System (GIS) based approach to using observed and projected data, for decision-making by non-expert government authorities, and 2) to document how DMT can be used to provide specialized yet understandable climate change information to assist local decision-makers in clarifying regional priorities within a wide array of adaptation options. The DMT combines climate change mapping, statistical GIS, and a vulnerability assessment. Okutama-machi, a 225.63 km2 sparsely populated mountainous region (2012 population 5,856) northwest of Tokyo, Japan, was chosen for this pilot study. In this paper, the most recent regional climate projections (5 km resolution) are transcribed into an understandable form for use by non-expert citizens who use the GIS-based DMT. Results illustrate qualitative agreement in projection of summer daily mean temperatures; the mean temperature increase at Okutama-machi is the greatest of any area in Tokyo. In comparing near future and future conditions, August monthly mean temperature will increase more than 0.7–0.9 °C and 2.8–2.9 °C, and monthly precipitation by 50 % and 25–41 %, respectively. However, the root mean square (RMS) errors and bias of percentage change for monthly precipitation in summertime are 26.8 % and 4.3 %, respectively. These data provide an early warning and have implications for local climate policy response.